Anomaly detection and reporting in a network assurance appliance

ABSTRACT

Systems, methods, and computer-readable media for detecting and reporting anomalies in a network environment for providing network assurance. In some embodiments, a system can determine confidence scores for at least one value of parameters of a network environment defining network events occurring in the network environment. The confidences scores can indicate a frequency that the defined network events have a specific event state. The confidence scores can be monitored to detect an anomaly in the network environment. In response to detecting the anomaly in the network environment, the system can determine a relevant network state of the network environment. The relevant network state of the network environment and the anomaly in the network environment can be presented to a user.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. Non-provisional patent application Ser. No. 16/813,358, filed Mar. 9, 2020, which is a continuation of U.S. Non-provisional patent application Ser. No. 15/702,374, filed Sep. 12, 2017, now U.S. Pat. No. 10,587,484, the full disclosures of which are incorporated herein by reference in their entireties.

TECHNICAL FIELD

The present technology pertains to network analytics, and more specifically to detecting and reporting anomalies of a network environment for providing network assurance.

BACKGROUND

In a network environment, sensors can be placed at various devices or elements in the network to collect flow data and network statistics from different locations. The collected data from the sensors can be analyzed to monitor and troubleshoot the network. The data collected from the sensors can provide valuable details about the status, security, or performance of the network, as well as any network elements. Information about the sensors can also help interpret the data from the sensors, in order to infer or ascertain additional details from the collected data. For example, understanding the placement of a sensor relative to other sensors in the network can provide a context to the data reported by the sensors, which can further help identify specific patterns or conditions in the network. As network environments increase in size and complexity a large amount of data is collected and generated in monitoring the network environments. Unfortunately, larger amounts of data generated for network environments make it more difficult to analyze the data and subsequently monitor network environments to determine anomalies in the network environments. Moreover, as states of network environments change after an anomaly occurs, often before an administrator can determine a network state of the environment at the time of the anomaly, it can be difficult for administrators to correctly diagnose and fix problems in the network environments.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to describe the manner in which the above-recited and other advantages and features of the disclosure can be obtained, a more particular description of the principles briefly described above will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings. Understanding that these drawings depict only exemplary embodiments of the disclosure and are not therefore to be considered to be limiting of its scope, the principles herein are described and explained with additional specificity and detail through the use of the accompanying drawings in which:

FIGS. 1A and 1B illustrate example network environments;

FIG. 2A illustrates an example object model for a network;

FIG. 2B illustrates an example object model for a tenant object in the example object model from FIG. 2A;

FIG. 2C illustrates an example association of various objects in the example object model from FIG. 2A;

FIG. 2D illustrates a schematic diagram of example models for implementing the example object model from FIG. 2A;

FIG. 3A illustrates an example network assurance appliance;

FIG. 3B illustrates an example system for network assurance;

FIG. 3C illustrates a schematic diagram of an example system for static policy analysis in a network.

FIG. 4 illustrates an example method embodiment for network assurance;

FIG. 5 illustrates an example event clustering system;

FIG. 6 illustrates another example method embodiment for network assurance;

FIG. 7 illustrates an example anomaly detection and reporting system;

FIG. 8 illustrates an example method for detecting and reporting anomalies in a network environment;

FIG. 9 illustrates an example network device in accordance with various embodiments; and

FIG. 10 illustrates an example computing device in accordance with various embodiments.

DESCRIPTION OF EXAMPLE EMBODIMENTS

Various embodiments of the disclosure are discussed in detail below. While specific implementations are discussed, it should be understood that this is done for illustration purposes only. A person skilled in the relevant art will recognize that other components and configurations may be used without parting from the spirit and scope of the disclosure.

Various embodiments of the disclosure are discussed in detail below. While specific implementations are discussed, it should be understood that this is done for illustration purposes only. A person skilled in the relevant art will recognize that other components and configurations may be used without parting from the spirit and scope of the disclosure. Thus, the following description and drawings are illustrative and are not to be construed as limiting. Numerous specific details are described to provide a thorough understanding of the disclosure. However, in certain instances, well-known or conventional details are not described in order to avoid obscuring the description. References to one or an embodiment in the present disclosure can be references to the same embodiment or any embodiment; and, such references mean at least one of the embodiments.

Reference to “one embodiment” or “an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the disclosure. The appearances of the phrase “in one embodiment” in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Moreover, various features are described which may be exhibited by some embodiments and not by others.

The terms used in this specification generally have their ordinary meanings in the art, within the context of the disclosure, and in the specific context where each term is used. Alternative language and synonyms may be used for any one or more of the terms discussed herein, and no special significance should be placed upon whether or not a term is elaborated or discussed herein. In some cases, synonyms for certain terms are provided. A recital of one or more synonyms does not exclude the use of other synonyms. The use of examples anywhere in this specification including examples of any terms discussed herein is illustrative only, and is not intended to further limit the scope and meaning of the disclosure or of any example term. Likewise, the disclosure is not limited to various embodiments given in this specification.

Without intent to limit the scope of the disclosure, examples of instruments, apparatus, methods and their related results according to the embodiments of the present disclosure are given below. Note that titles or subtitles may be used in the examples for convenience of a reader, which in no way should limit the scope of the disclosure. Unless otherwise defined, technical and scientific terms used herein have the meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains. In the case of conflict, the present document, including definitions will control.

Additional features and advantages of the disclosure will be set forth in the description which follows, and in part will be obvious from the description, or can be learned by practice of the herein disclosed principles. The features and advantages of the disclosure can be realized and obtained by means of the instruments and combinations particularly pointed out in the appended claims. These and other features of the disclosure will become more fully apparent from the following description and appended claims, or can be learned by the practice of the principles set forth herein.

Overview

Additional features and advantages of the disclosure will be set forth in the description which follows, and in part will be obvious from the description, or can be learned by practice of the herein disclosed principles. The features and advantages of the disclosure can be realized and obtained by means of the instruments and combinations particularly pointed out in the appended claims. These and other features of the disclosure will become more fully apparent from the following description and appended claims, or can be learned by the practice of the principles set forth herein.

A system can determine confidence scores for at least one value of parameters of a network environment defining network events occurring in the network environment. The confidences scores can indicate a frequency that the defined network events have a specific event state. Additionally, the confidence scores can be monitored to detect an anomaly in the network environment. In response to detecting the anomaly in the network environment, the system can determine a relevant network state of the network environment. The relevant network state of the network environment and the anomaly in the network environment can be presented to a user.

Confidence scores indicating a frequency that network events defined by values of parameters of a network environment have a specific event state can be monitored to identify an anomaly in the network environment. The identified anomaly in the network environment can be used to identify a relevant network state of the network environment in response to detecting the anomaly in the network environment. The relevant network state, identified using the anomaly, can be presented, along with the identified anomaly, to a user.

Confidence scores indicating a frequency that network events defined by values of parameters of a network environment have a specific event state can be monitored to identify an anomaly in the network environment. The values of the parameters of the network environment defining the network events used in detecting the anomaly can be used to identify a relevant network state of the network environment. The relevant network state identified using the values of the parameters of the network environment can be presented, along with the identified anomaly, to a user.

Example Embodiments

The disclosed technology addresses the need in the art for providing network assurance. The present technology involves system, methods, and computer-readable media for detecting and reporting anomalies in a network environment in providing network assurance. The present technology will be described in the following disclosure as follows. The discussion begins with an introductory discussion of network assurance and a description of example computing environments, as illustrated in FIGS. 1A and 1B. A discussion of network models for network assurance, as shown in FIGS. 2A through 2D, and network assurance systems and methods, as shown in FIGS. 3A-C, 4, 5, and 6 will then follow. The discussion continues with a description and examples of detecting and reporting anomalies in a network environment, as shown in FIGS. 7 and 8. The discussion concludes with a description of an example network device, as illustrated in FIG. 9, and an example computing device, as illustrated in FIG. 10, including example hardware components suitable for hosting software applications and performing computing operations. The disclosure now turns to an introductory discussion of network assurance.

Network assurance is the guarantee or determination that the network is behaving as intended by the network operator and has been configured properly (e.g., the network is doing network and individual network elements (e.g., switches, routers, applications, resources, etc.). However, often times, the configurations, policies, etc., defined by a network operator are incorrect or not accurately reflected in the actual behavior of the network. For example, a network operator specifies a configuration A for one or more types of traffic but later finds out that the network is actually applying configuration B to that traffic or otherwise processing that traffic in a manner that is inconsistent with configuration A. This can be a result of many different causes, such as hardware errors, software bugs, varying priorities, configuration conflicts, misconfiguration of one or more settings, improper rule rendering by devices, unexpected errors or events, software upgrades, configuration changes, failures, etc. As another example, a network operator implements configuration C but one or more other configurations result in the network behaving in a manner that is inconsistent with the intent reflected by the implementation of configuration C. For example, such a situation can result when configuration C conflicts with other configurations in the network.

The approaches herein can provide network assurance by modeling various aspects of the network and/or performing consistency checks as well as other network assurance checks. The network assurance approaches herein can be implemented in various types of networks, including a private network, such as a local area network (LAN); an enterprise network; a standalone or traditional network, such as a data center network; a network including a physical or underlay layer and a logical or overlay layer, such as a VXLAN or software-defined network (SDN) (e.g., Application Centric Infrastructure (ACI) or VMware NSX networks); etc.

Network models can be constructed for a network and implemented for network assurance. A network model can provide a representation of one or more aspects of a network, including, without limitation the network's policies, configurations, requirements, security, routing, topology, applications, hardware, filters, contracts, access control lists, infrastructure, etc. As will be further explained below, different types of models can be generated for a network.

Such models can be implemented to ensure that the behavior of the network will be consistent (or is consistent) with the intended behavior reflected through specific configurations (e.g., policies, settings, definitions, etc.) implemented by the network operator. Unlike traditional network monitoring, which involves sending and analyzing data packets and observing network behavior, network assurance can be performed through modeling without necessarily ingesting packet data or monitoring traffic or network behavior. This can result in foresight, insight, and hindsight: problems can be prevented before they occur, identified when they occur, and fixed immediately after they occur.

Thus, network assurance can involve modeling properties of the network to deterministically predict the behavior of the network. The network can be determined to be healthy if the model(s) indicate proper behavior (e.g., no inconsistencies, conflicts, errors, etc.). The network can be determined to be functional, but not fully healthy, if the modeling indicates proper behavior but some inconsistencies. The network can be determined to be non-functional and not healthy if the modeling indicates improper behavior and errors. If inconsistencies or errors are detected by the modeling, a detailed analysis of the corresponding model(s) can allow one or more underlying or root problems to be identified with great accuracy.

The modeling can consume numerous types of smart events which model a large amount of behavioral aspects of the network. Smart events can impact various aspects of the network, such as underlay services, overlay services, tenant connectivity, tenant security, tenant endpoint (EP) mobility, tenant policy, tenant routing, resources, etc.

Having described various aspects of network assurance, the disclosure now turns to a discussion of example network environments for network assurance.

FIG. 1A illustrates a diagram of an example Network Environment 100, such as a data center. The Network Environment 100 can include a Fabric 120 which can represent the physical layer or infrastructure (e.g., underlay) of the Network Environment 100. Fabric 120 can include Spines 102 (e.g., spine routers or switches) and Leafs 104 (e.g., leaf routers or switches) which can be interconnected for routing or switching traffic in the Fabric 120. Spines 102 can interconnect Leafs 104 in the Fabric 120, and Leafs 104 can connect the Fabric 120 to an overlay or logical portion of the Network Environment 100, which can include application services, servers, virtual machines, containers, endpoints, etc. Thus, network connectivity in the Fabric 120 can flow from Spines 102 to Leafs 104, and vice versa. The interconnections between Leafs 104 and Spines 102 can be redundant (e.g., multiple interconnections) to avoid a failure in routing. In some embodiments, Leafs 104 and Spines 102 can be fully connected, such that any given Leaf is connected to each of the Spines 102, and any given Spine is connected to each of the Leafs 104. Leafs 104 can be, for example, top-of-rack (“ToR”) switches, aggregation switches, gateways, ingress and/or egress switches, provider edge devices, and/or any other type of routing or switching device.

Leafs 104 can be responsible for routing and/or bridging tenant or customer packets and applying network policies or rules. Network policies and rules can be driven by one or more Controllers 116, and/or implemented or enforced by one or more devices, such as Leafs 104. Leafs 104 can connect other elements to the Fabric 120. For example, Leafs 104 can connect Servers 106, Hypervisors 108, Virtual Machines (VMs) 110, Applications 112, Network Device 114, etc., with Fabric 120. Such elements can reside in one or more logical or virtual layers or networks, such as an overlay network. In some cases, Leafs 104 can encapsulate and decapsulate packets to and from such elements (e.g., Servers 106) in order to enable communications throughout Network Environment 100 and Fabric 120. Leafs 104 can also provide any other devices, services, tenants, or workloads with access to Fabric 120. In some cases, Servers 106 connected to Leafs 104 can similarly encapsulate and decapsulate packets to and from Leafs 104. For example, Servers 106 can include one or more virtual switches or routers or tunnel endpoints for tunneling packets between an overlay or logical layer hosted by, or connected to, Servers 106 and an underlay layer represented by Fabric 120 and accessed via Leafs 104.

Applications 112 can include software applications, services, containers, appliances, functions, service chains, etc. For example, Applications 112 can include a firewall, a database, a CDN server, an IDS/IPS, a deep packet inspection service, a message router, a virtual switch, etc. An application from Applications 112 can be distributed, chained, or hosted by multiple endpoints (e.g., Servers 106, VMs 110, etc.), or may run or execute entirely from a single endpoint.

VMs 110 can be virtual machines hosted by Hypervisors 108 or virtual machine managers running on Servers 106. VMs 110 can include workloads running on a guest operating system on a respective server. Hypervisors 108 can provide a layer of software, firmware, and/or hardware that creates, manages, and/or runs the VMs 110. Hypervisors 108 can allow VMs 110 to share hardware resources on Servers 106, and the hardware resources on Servers 106 to appear as multiple, separate hardware platforms. Moreover, Hypervisors 108 on Servers 106 can host one or more VMs 110.

In some cases, VMs 110 and/or Hypervisors 108 can be migrated to other Servers 106. Servers 106 can similarly be migrated to other locations in Network Environment 100. For example, a server connected to a specific leaf can be changed to connect to a different or additional leaf. Such configuration or deployment changes can involve modifications to settings, configurations and policies that are applied to the resources being migrated as well as other network components.

In some cases, one or more Servers 106, Hypervisors 108, and/or VMs 110 can represent or reside in a tenant or customer space. Tenant space can include workloads, services, applications, devices, networks, and/or resources that are associated with one or more clients or subscribers. Accordingly, traffic in Network Environment 100 can be routed based on specific tenant policies, spaces, agreements, configurations, etc. Moreover, addressing can vary between one or more tenants. In some configurations, tenant spaces can be divided into logical segments and/or networks and separated from logical segments and/or networks associated with other tenants. Addressing, policy, security and configuration information between tenants can be managed by Controllers 116, Servers 106, Leafs 104, etc.

Configurations in Network Environment 100 can be implemented at a logical level, a hardware level (e.g., physical), and/or both. For example, configurations can be implemented at a logical and/or hardware level based on endpoint or resource attributes, such as endpoint types and/or application groups or profiles, through a software-defined network (SDN) framework (e.g., Application-Centric Infrastructure (ACI) or VMWARE NSX). To illustrate, one or more administrators can define configurations at a logical level (e.g., application or software level) through Controllers 116, which can implement or propagate such configurations through Network Environment 100. In some examples, Controllers 116 can be Application Policy Infrastructure Controllers (APICs) in an ACI framework. In other examples, Controllers 116 can be one or more management components for associated with other SDN solutions, such as NSX Managers.

Such configurations can define rules, policies, priorities, protocols, attributes, objects, etc., for routing and/or classifying traffic in Network Environment 100. For example, such configurations can define attributes and objects for classifying and processing traffic based on Endpoint Groups (EPGs), Security Groups (SGs), VM types, bridge domains (BDs), virtual routing and forwarding instances (VRFs), tenants, priorities, firewall rules, etc. Other example network objects and configurations are further described below. Traffic policies and rules can be enforced based on tags, attributes, or other characteristics of the traffic, such as protocols associated with the traffic, EPGs associated with the traffic, SGs associated with the traffic, network address information associated with the traffic, etc. Such policies and rules can be enforced by one or more elements in Network Environment 100, such as Leafs 104, Servers 106, Hypervisors 108, Controllers 116, etc. As previously explained, Network Environment 100 can be configured according to one or more particular software-defined network (SDN) solutions, such as CISCO ACI or VMWARE NSX. These example SDN solutions are briefly described below.

ACI can provide an application-centric or policy-based solution through scalable distributed enforcement. ACI supports integration of physical and virtual environments under a declarative configuration model for networks, servers, services, security, requirements, etc. For example, the ACI framework implements EPGs, which can include a collection of endpoints or applications that share common configuration requirements, such as security, QoS, services, etc. Endpoints can be virtual/logical or physical devices, such as VMs, containers, hosts, or physical servers that are connected to Network Environment 100. Endpoints can have one or more attributes such as a VM name, guest OS name, a security tag, application profile, etc. Application configurations can be applied between EPGs, instead of endpoints directly, in the form of contracts. Leafs 104 can classify incoming traffic into different EPGs. The classification can be based on, for example, a network segment identifier such as a VLAN ID, VXLAN Network Identifier (VNID), NVGRE Virtual Subnet Identifier (VSID), MAC address, IP address, etc.

In some cases, classification in the ACI infrastructure can be implemented by Application Virtual Switches (AVS), which can run on a host, such as a server or switch. For example, an AVS can classify traffic based on specified attributes, and tag packets of different attribute EPGs with different identifiers, such as network segment identifiers (e.g., VLAN ID). Finally, Leafs 104 can tie packets with their attribute EPGs based on their identifiers and enforce policies, which can be implemented and/or managed by one or more Controllers 116. Leaf 104 can classify to which EPG the traffic from a host belongs and enforce policies accordingly.

Another example SDN solution is based on VMWARE NSX. With VMWARE NSX, hosts can run a distributed firewall (DFW) which can classify and process traffic. Consider a case where three types of VMs, namely, application, database and web VMs, are put into a single layer-2 network segment. Traffic protection can be provided within the network segment based on the VM type. For example, HTTP traffic can be allowed among web VMs, and disallowed between a web VM and an application or database VM. To classify traffic and implement policies, VMWARE NSX can implement security groups, which can be used to group the specific VMs (e.g., web VMs, application VMs, database VMs). DFW rules can be configured to implement policies for the specific security groups. To illustrate, in the context of the previous example, DFW rules can be configured to block HTTP traffic between web, application, and database security groups.

Returning now to FIG. 1A, Network Environment 100 can deploy different hosts via Leafs 104, Servers 106, Hypervisors 108, VMs 110, Applications 112, and Controllers 116, such as VMWARE ESXi hosts, WINDOWS HYPER-V hosts, bare metal physical hosts, etc. Network Environment 100 may interoperate with a variety of Hypervisors 108, Servers 106 (e.g., physical and/or virtual servers), SDN orchestration platforms, etc. Network Environment 100 may implement a declarative model to allow its integration with application design and holistic network policy.

Controllers 116 can provide centralized access to fabric information, application configuration, resource configuration, application-level configuration modeling for a software-defined network (SDN) infrastructure, integration with management systems or servers, etc. Controllers 116 can form a control plane that interfaces with an application plane via northbound APIs and a data plane via southbound APIs.

As previously noted, Controllers 116 can define and manage application-level model(s) for configurations in Network Environment 100. In some cases, application or device configurations can also be managed and/or defined by other components in the network. For example, a hypervisor or virtual appliance, such as a VM or container, can run a server or management tool to manage software and services in Network Environment 100, including configurations and settings for virtual appliances.

As illustrated above, Network Environment 100 can include one or more different types of SDN solutions, hosts, etc. For the sake of clarity and explanation purposes, various examples in the disclosure will be described with reference to an ACI framework, and Controllers 116 may be interchangeably referenced as controllers, APICs, or APIC controllers. However, it should be noted that the technologies and concepts herein are not limited to ACI solutions and may be implemented in other architectures and scenarios, including other SDN solutions as well as other types of networks which may not deploy an SDN solution.

Further, as referenced herein, the term “hosts” can refer to Servers 106 (e.g., physical or logical), Hypervisors 108, VMs 110, containers (e.g., Applications 112), etc., and can run or include any type of server or application solution. Non-limiting examples of “hosts” can include virtual switches or routers, such as distributed virtual switches (DVS), application virtual switches (AVS), vector packet processing (VPP) switches; VCENTER and NSX MANAGERS; bare metal physical hosts; HYPER-V hosts; VMs; DOCKER Containers; etc.

FIG. 1B illustrates another example of Network Environment 100. In this example, Network Environment 100 includes Endpoints 122 connected to Leafs 104 in Fabric 120. Endpoints 122 can be physical and/or logical or virtual entities, such as servers, clients, VMs, hypervisors, software containers, applications, resources, network devices, workloads, etc. For example, an Endpoint 122 can be an object that represents a physical device (e.g., server, client, switch, etc.), an application (e.g., web application, database application, etc.), a logical or virtual resource (e.g., a virtual switch, a virtual service appliance, a virtualized network function (VNF), a VM, a service chain, etc.), a container running a software resource (e.g., an application, an appliance, a VNF, a service chain, etc.), storage, a workload or workload engine, etc. Endpoints 122 can have an address (e.g., an identity), a location (e.g., host, network segment, virtual routing and forwarding (VRF) instance, domain, etc.), one or more attributes (e.g., name, type, version, patch level, OS name, OS type, etc.), a tag (e.g., security tag), a profile, etc.

Endpoints 122 can be associated with respective Logical Groups 118. Logical Groups 118 can be logical entities containing endpoints (physical and/or logical or virtual) grouped together according to one or more attributes, such as endpoint type (e.g., VM type, workload type, application type, etc.), one or more requirements (e.g., policy requirements, security requirements, QoS requirements, customer requirements, resource requirements, etc.), a resource name (e.g., VM name, application name, etc.), a profile, platform or operating system (OS) characteristics (e.g., OS type or name including guest and/or host OS, etc.), an associated network or tenant, one or more policies, a tag, etc. For example, a logical group can be an object representing a collection of endpoints grouped together. To illustrate, Logical Group 1 can contain client endpoints, Logical Group 2 can contain web server endpoints, Logical Group 3 can contain application server endpoints, Logical Group N can contain database server endpoints, etc. In some examples, Logical Groups 118 are EPGs in an ACI environment and/or other logical groups (e.g., SGs) in another SDN environment.

Traffic to and/or from Endpoints 122 can be classified, processed, managed, etc., based Logical Groups 118. For example, Logical Groups 118 can be used to classify traffic to or from Endpoints 122, apply policies to traffic to or from Endpoints 122, define relationships between Endpoints 122, define roles of Endpoints 122 (e.g., whether an endpoint consumes or provides a service, etc.), apply rules to traffic to or from Endpoints 122, apply filters or access control lists (ACLs) to traffic to or from Endpoints 122, define communication paths for traffic to or from Endpoints 122, enforce requirements associated with Endpoints 122, implement security and other configurations associated with Endpoints 122, etc.

In an ACI environment, Logical Groups 118 can be EPGs used to define contracts in the ACI. Contracts can include rules specifying what and how communications between EPGs take place. For example, a contract can define what provides a service, what consumes a service, and what policy objects are related to that consumption relationship. A contract can include a policy that defines the communication path and all related elements of a communication or relationship between endpoints or EPGs. For example, a Web EPG can provide a service that a Client EPG consumes, and that consumption can be subject to a filter (ACL) and a service graph that includes one or more services, such as firewall inspection services and server load balancing.

FIG. 2A illustrates a diagram of an example Management Information Model 200 for an SDN network, such as Network Environment 100. The following discussion of Management Information Model 200 references various terms which shall also be used throughout the disclosure. Accordingly, for clarity, the disclosure shall first provide below a list of terminology, which will be followed by a more detailed discussion of Management Information Model 200.

As used herein, an “Alias” can refer to a changeable name for a given object. Thus, even if the name of an object, once created, cannot be changed, the Alias can be a field that can be changed.

As used herein, the term “Aliasing” can refer to a rule (e.g., contracts, policies, configurations, etc.) that overlaps one or more other rules. For example, Contract 1 defined in a logical model of a network can be said to be aliasing Contract 2 defined in the logical model of the network if Contract 1 overlaps Contract 1. In this example, by aliasing Contract 2, Contract 1 may render Contract 2 redundant or inoperable. For example, if Contract 1 has a higher priority than Contract 2, such aliasing can render Contract 2 redundant based on Contract 1's overlapping and higher priority characteristics.

As used herein, the term “APIC” can refer to one or more controllers (e.g., Controllers 116) in an ACI framework. The APIC can provide a unified point of automation and management, policy programming, application deployment, health monitoring for an ACI multitenant fabric. The APIC can be implemented as a single controller, a distributed controller, or a replicated, synchronized, and/or clustered controller.

As used herein, the term “BDD” can refer to a binary decision tree. A binary decision tree can be a data structure representing functions, such as Boolean functions.

As used herein, the term “BD” can refer to a bridge domain. A bridge domain can be a set of logical ports that share the same flooding or broadcast characteristics. Like a virtual LAN (VLAN), bridge domains can span multiple devices. A bridge domain can be a L2 (Layer 2) construct.

As used herein, a “Consumer” can refer to an endpoint, resource, and/or EPG that consumes a service.

As used herein, a “Context” can refer to an L3 (Layer 3) address domain that allows multiple instances of a routing table to exist and work simultaneously. This increases functionality by allowing network paths to be segmented without using multiple devices. Non-limiting examples of a context or L3 address domain can include a Virtual Routing and Forwarding (VRF) instance, a private network, and so forth.

As used herein, the term “Contract” can refer to rules or configurations that specify what and how communications in a network are conducted (e.g., allowed, denied, filtered, processed, etc.). In an ACI network, contracts can specify how communications between endpoints and/or EPGs take place. In some examples, a contract can provide rules and configurations akin to an Access Control List (ACL).

As used herein, the term “Distinguished Name” (DN) can refer to a unique name that describes an object, such as an MO, and locates its place in Management Information Model 200. In some cases, the DN can be (or equate to) a Fully Qualified Domain Name (FQDN).

As used herein, the term “Endpoint Group” (EPG) can refer to a logical entity or object associated with a collection or group of endpoints as previously described with reference to FIG. 1B.

As used herein, the term “Filter” can refer to a parameter or configuration for allowing communications. For example, in a whitelist model where all communications are blocked by default, a communication must be given explicit permission to prevent such communication from being blocked. A filter can define permission(s) for one or more communications or packets. A filter can thus function similar to an ACL or Firewall rule. In some examples, a filter can be implemented in a packet (e.g., TCP/IP) header field, such as L3 protocol type, L4 (Layer 4) ports, and so on, which is used to allow inbound or outbound communications between endpoints or EPGs, for example.

As used herein, the term “L2 Out” can refer to a bridged connection. A bridged connection can connect two or more segments of the same network so that they can communicate. In an ACI framework, an L2 out can be a bridged (Layer 2) connection between an ACI fabric (e.g., Fabric 120) and an outside Layer 2 network, such as a switch.

As used herein, the term “L3 Out” can refer to a routed connection. A routed Layer 3 connection uses a set of protocols that determine the path that data follows in order to travel across networks from its source to its destination. Routed connections can perform forwarding (e.g., IP forwarding) according to a protocol selected, such as BGP (border gateway protocol), OSPF (Open Shortest Path First), EIGRP (Enhanced Interior Gateway Routing Protocol), etc.

As used herein, the term “Managed Object” (MO) can refer to an abstract representation of objects that are managed in a network (e.g., Network Environment 100). The objects can be concrete objects (e.g., a switch, server, adapter, etc.), or logical objects (e.g., an application profile, an EPG, a fault, etc.). The MOs can be network resources or elements that are managed in the network. For example, in an ACI environment, an MO can include an abstraction of an ACI fabric (e.g., Fabric 120) resource.

As used herein, the term “Management Information Tree” (MIT) can refer to a hierarchical management information tree containing the MOs of a system. For example, in ACI, the MIT contains the MOs of the ACI fabric (e.g., Fabric 120). The MIT can also be referred to as a Management Information Model (MIM), such as Management Information Model 200.

As used herein, the term “Policy” can refer to one or more specifications for controlling some aspect of system or network behavior. For example, a policy can include a named entity that contains specifications for controlling some aspect of system behavior. To illustrate, a Layer 3 Outside Network Policy can contain the BGP protocol to enable BGP routing functions when connecting Fabric 120 to an outside Layer 3 network.

As used herein, the term “Profile” can refer to the configuration details associated with a policy. For example, a profile can include a named entity that contains the configuration details for implementing one or more instances of a policy. To illustrate, a switch node profile for a routing policy can contain the switch-specific configuration details to implement the BGP routing protocol.

As used herein, the term “Provider” refers to an object or entity providing a service. For example, a provider can be an EPG that provides a service.

As used herein, the term “Subject” refers to one or more parameters in a contract for defining communications. For example, in ACI, subjects in a contract can specify what information can be communicated and how. Subjects can function similar to ACLs.

As used herein, the term “Tenant” refers to a unit of isolation in a network. For example, a tenant can be a secure and exclusive virtual computing environment. In ACI, a tenant can be a unit of isolation from a policy perspective, but does not necessarily represent a private network. Indeed, ACI tenants can contain multiple private networks (e.g., VRFs). Tenants can represent a customer in a service provider setting, an organization or domain in an enterprise setting, or just a grouping of policies.

As used herein, the term “VRF” refers to a virtual routing and forwarding instance. The VRF can define a Layer 3 address domain that allows multiple instances of a routing table to exist and work simultaneously. This increases functionality by allowing network paths to be segmented without using multiple devices. Also known as a context or private network.

Having described various terms used herein, the disclosure now returns to a discussion of Management Information Model (MIM) 200 in FIG. 2A. As previously noted, MIM 200 can be a hierarchical management information tree or MIT. Moreover, MIM 200 can be managed and processed by Controllers 116, such as APICs in an ACI. Controllers 116 can enable the control of managed resources by presenting their manageable characteristics as object properties that can be inherited according to the location of the object within the hierarchical structure of the model.

The hierarchical structure of MIM 200 starts with Policy Universe 202 at the top (Root) and contains parent and child nodes 116, 204, 206, 208, 210, 212. Nodes 116, 202, 204, 206, 208, 210, 212 in the tree represent the managed objects (MOs) or groups of objects. Each object in the fabric (e.g., Fabric 120) has a unique distinguished name (DN) that describes the object and locates its place in the tree. The Nodes 116, 202, 204, 206, 208, 210, 212 can include the various MOs, as described below, which contain policies that govern the operation of the system.

Controllers 116

Controllers 116 (e.g., APIC controllers) can provide management, policy programming, application deployment, and health monitoring for Fabric 120.

Node 204

Node 204 includes a tenant container for policies that enable an administrator to exercise domain-based access control. Non-limiting examples of tenants can include:

User tenants defined by the administrator according to the needs of users. They contain policies that govern the operation of resources such as applications, databases, web servers, network-attached storage, virtual machines, and so on.

The common tenant is provided by the system but can be configured by the administrator. It contains policies that govern the operation of resources accessible to all tenants, such as firewalls, load balancers, Layer 4 to Layer 7 services, intrusion detection appliances, and so on.

The infrastructure tenant is provided by the system but can be configured by the administrator. It contains policies that govern the operation of infrastructure resources such as the fabric overlay (e.g., VXLAN). It also enables a fabric provider to selectively deploy resources to one or more user tenants. Infrastructure tenant polices can be configurable by the administrator.

The management tenant is provided by the system but can be configured by the administrator. It contains policies that govern the operation of fabric management functions used for in-band and out-of-band configuration of fabric nodes. The management tenant contains a private out-of-bound address space for the Controller/Fabric internal communications that is outside the fabric data path that provides access through the management port of the switches. The management tenant enables discovery and automation of communications with virtual machine controllers.

Node 206

Node 206 can contain access policies that govern the operation of switch access ports that provide connectivity to resources such as storage, compute, Layer 2 and Layer 3 (bridged and routed) connectivity, virtual machine hypervisors, Layer 4 to Layer 7 devices, and so on. If a tenant requires interface configurations other than those provided in the default link, Cisco Discovery Protocol (CDP), Link Layer Discovery Protocol (LLDP), Link Aggregation Control Protocol (LACP), or Spanning Tree Protocol (STP), an administrator can configure access policies to enable such configurations on the access ports of Leafs 104.

Node 206 can contain fabric policies that govern the operation of the switch fabric ports, including such functions as Network Time Protocol (NTP) server synchronization, Intermediate System-to-Intermediate System Protocol (IS-IS), Border Gateway Protocol (BGP) route reflectors, Domain Name System (DNS) and so on. The fabric MO contains objects such as power supplies, fans, chassis, and so on.

Node 208

Node 208 can contain VM domains that group VM controllers with similar networking policy requirements. VM controllers can share virtual space (e.g., VLAN or VXLAN space) and application EPGs. Controllers 116 communicate with the VM controller to publish network configurations such as port groups that are then applied to the virtual workloads.

Node 210

Node 210 can contain Layer 4 to Layer 7 service integration life cycle automation framework that enables the system to dynamically respond when a service comes online or goes offline. Policies can provide service device package and inventory management functions.

Node 212

Node 212 can contain access, authentication, and accounting (AAA) policies that govern user privileges, roles, and security domains of Fabric 120.

The hierarchical policy model can fit well with an API, such as a REST API interface. When invoked, the API can read from or write to objects in the MIT. URLs can map directly into distinguished names that identify objects in the MIT. Data in the MIT can be described as a self-contained structured tree text document encoded in XML or JSON, for example.

FIG. 2B illustrates an example object model 220 for a tenant portion of MIM 200. As previously noted, a tenant is a logical container for application policies that enable an administrator to exercise domain-based access control. A tenant thus represents a unit of isolation from a policy perspective, but it does not necessarily represent a private network. Tenants can represent a customer in a service provider setting, an organization or domain in an enterprise setting, or just a convenient grouping of policies. Moreover, tenants can be isolated from one another or can share resources.

Tenant portion 204A of MIM 200 can include various entities, and the entities in Tenant Portion 204A can inherit policies from parent entities. Non-limiting examples of entities in Tenant Portion 204A can include Filters 240, Contracts 236, Outside Networks 222, Bridge Domains 230, VRF Instances 234, and Application Profiles 224.

Bridge Domains 230 can include Subnets 232. Contracts 236 can include Subjects 238. Application Profiles 224 can contain one or more EPGs 226. Some applications can contain multiple components. For example, an e-commerce application could require a web server, a database server, data located in a storage area network, and access to outside resources that enable financial transactions. Application Profile 224 contains as many (or as few) EPGs as necessary that are logically related to providing the capabilities of an application.

EPG 226 can be organized in various ways, such as based on the application they provide, the function they provide (such as infrastructure), where they are in the structure of the data center (such as DMZ), or whatever organizing principle that a fabric or tenant administrator chooses to use.

EPGs in the fabric can contain various types of EPGs, such as application EPGs, Layer 2 external outside network instance EPGs, Layer 3 external outside network instance EPGs, management EPGs for out-of-band or in-band access, etc. EPGs 226 can also contain Attributes 228, such as encapsulation-based EPGs, IP-based EPGs, or MAC-based EPGs.

As previously mentioned, EPGs can contain endpoints (e.g., EPs 122) that have common characteristics or attributes, such as common policy requirements (e.g., security, virtual machine mobility (VMM), QoS, or Layer 4 to Layer 7 services). Rather than configure and manage endpoints individually, they can be placed in an EPG and managed as a group.

Policies apply to EPGs, including the endpoints they contain. An EPG can be statically configured by an administrator in Controllers 116, or dynamically configured by an automated system such as VCENTER or OPENSTACK.

To activate tenant policies in Tenant Portion 204A, fabric access policies should be configured and associated with tenant policies. Access policies enable an administrator to configure other network configurations, such as port channels and virtual port channels, protocols such as LLDP, CDP, or LACP, and features such as monitoring or diagnostics.

FIG. 2C illustrates an example Association 260 of tenant entities and access entities in MIM 200. Policy Universe 202 contains Tenant Portion 204A and Access Portion 206A. Thus, Tenant Portion 204A and Access Portion 206A are associated through Policy Universe 202.

Access Portion 206A can contain fabric and infrastructure access policies. Typically, in a policy model, EPGs are coupled with VLANs. For traffic to flow, an EPG is deployed on a leaf port with a VLAN in a physical, VMM, L2 out, L3 out, or Fiber Channel domain, for example.

Access Portion 206A thus contains Domain Profile 236 which can define a physical, VMM, L2 out, L3 out, or Fiber Channel domain, for example, to be associated to the EPGs. Domain Profile 236 contains VLAN Instance Profile 238 (e.g., VLAN pool) and Attacheable Access Entity Profile (AEP) 240, which are associated directly with application EPGs. The AEP 240 deploys the associated application EPGs to the ports to which it is attached, and automates the task of assigning VLANs. While a large data center can have thousands of active VMs provisioned on hundreds of VLANs, Fabric 120 can automatically assign VLAN IDs from VLAN pools. This saves time compared with trunking down VLANs in a traditional data center.

FIG. 2D illustrates a schematic diagram of example models for implementing MIM 200. The network assurance models can include L_Model 270A (Logical Model), LR_Model 270B (Logical Rendered Model or Logical Runtime Model), L_Model 272 (Logical Model for i), Ci_Model 274 (Concrete model for i), and Hi_Model 276 (Hardware model or TCAM Model for i).

L_Model 270A is the logical representation of the objects and their relationships in MIM 200. L_Model 270A can be generated by Controllers 116 based on configurations entered in Controllers 116 for the network, and thus represents the configurations of the network at Controllers 116. This is the declaration of the “end-state” expression that is desired when the elements of the network entities (e.g., applications) are connected and Fabric 120 is provisioned by Controllers 116. In other words, because L_Model 270A represents the configurations entered in Controllers 116, including the objects and relationships in MIM 200, it can also reflect the “intent” of the administrator: how the administrator wants the network and network elements to behave.

LR_Model 270B is the abstract model expression that Controllers 116 (e.g., APICs in ACI) resolve from L_Model 270A. LR_Model 270B can thus provide the elemental configuration components that would be delivered to the physical infrastructure (e.g., Fabric 120) to execute one or more policies. For example, LR_Model 270B can be delivered to Leafs 104 in Fabric 120 to configure Leafs 104 for communication with attached Endpoints 122.

Li_Model 272 is a switch-level or switch-specific model obtained from Logical Model 270A and/or Resolved Model 270B. For example, Li_Model 272 can represent the portion of L_Model 270A and/or LR_Model 270B pertaining to a specific switch or router i. To illustrate, Li_Model 272 L₁ can represent the portion of L_Model 270A and/or LR_Model 270B pertaining to Leaf 1 (104). Thus, Li_Model 272 can be generated from L_Model 270A and/or LR_Model 270B for one or more switch or routers (e.g., Leafs 104 and/or Spines 102) on Fabric 120.

Ci_Model 274 is the actual in-state configuration at the individual fabric member i (e.g., switch i). In other words, Ci_Model 274 is a switch-level or switch-specific model that is based on Li_Model 272. For example, Controllers 116 can deliver Li_Model 272 to Leaf 1 (104). Leaf 1 (104) can take Li_Model 272, which can be specific to Leaf 1 (104), and render the policies in Li_Model 272 into a concrete model, Ci_Model 274, that runs on Leaf 1 (104). Leaf 1 (104) can render Li_Model 272 via the OS on Leaf 1 (104), for example. Thus, Ci_Model 274 can be analogous to compiled software, as it is the form of Li_Model 272 that the switch OS at Leaf 1 (104) can execute.

Hi_Model 276 is also a switch-level or switch-specific model for switch i, but is based on Ci_Model 274 for switch i. Hi_Model 276 is the actual configuration (e.g., rules) stored or rendered on the hardware or memory (e.g., TCAM memory) at the individual fabric member i (e.g., switch i). For example, Hi_Model 276 can represent the configurations (e.g., rules) which Leaf 1 (104) stores or renders on the hardware (e.g., TCAM memory) of Leaf 1 (104) based on Ci_Model 274 at Leaf 1 (104). The switch OS at Leaf 1 (104) can render or execute Ci_Model 274, and Leaf 1 (104) can store or render the configurations from Ci Model in storage, such as the memory or TCAM at Leaf 1 (104). The configurations from Hi_Model 276 stored or rendered by Leaf 1 (104) represent the configurations that will be implemented by Leaf 1 (104) when processing traffic.

While Models 272, 274, 276 are shown as device-specific models, similar models can be generated or aggregated for a collection of fabric members (e.g., Leafs 104 and/or Spines 102) in Fabric 120. When combined, device-specific models, such as Model 272, Model 274, and/or Model 276, can provide a representation of Fabric 120 that extends beyond a particular device. For example, in some cases, Li_Model 272, Ci Model 272, and/or Hi Model 272 associated with some or all individual fabric members (e.g., Leafs 104 and Spines 102) can be combined or aggregated to generate one or more aggregated models based on the individual fabric members.

As referenced herein, the terms H Model, T Model, and TCAM Model can be used interchangeably to refer to a hardware model, such as Hi_Model 276. For example, Ti Model, Hi Model and TCAMi Model may be used interchangeably to refer to Hi_Model 276.

Models 270A, 270B, 272, 274, 276 can provide representations of various aspects of the network or various configuration stages for MIM 200. For example, one or more of Models 270A, 270B, 272, 274, 276 can be used to generate Underlay Model 278 representing one or more aspects of Fabric 120 (e.g., underlay topology, routing, etc.), Overlay Model 280 representing one or more aspects of the overlay or logical segment(s) of Network Environment 100 (e.g., COOP, MPBGP, tenants, VRFs, VLANs, VXLANs, virtual applications, VMs, hypervisors, virtual switching, etc.), Tenant Model 282 representing one or more aspects of Tenant portion 204A in MIM 200 (e.g., security, forwarding, service chaining, QoS, VRFs, BDs, Contracts, Filters, EPGs, subnets, etc.), Resources Model 284 representing one or more resources in Network Environment 100 (e.g., storage, computing, VMs, port channels, physical elements, etc.), etc.

In general, L_Model 270A can be the high-level expression of what exists in the LR_Model 270B, which should be present on the concrete devices as Ci_Model 274 and Hi_Model 276 expression. If there is any gap between the models, there may be inconsistent configurations or problems.

FIG. 3A illustrates a diagram of an example Assurance Appliance 300 for network assurance. In this example, Assurance Appliance 300 can include k VMs 110 operating in cluster mode. VMs are used in this example for explanation purposes. However, it should be understood that other configurations are also contemplated herein, such as use of containers, bare metal devices, Endpoints 122, or any other physical or logical systems. Moreover, while FIG. 3A illustrates a cluster mode configuration, other configurations are also contemplated herein, such as a single mode configuration (e.g., single VM, container, or server) or a service chain for example.

Assurance Appliance 300 can run on one or more Servers 106, VMs 110, Hypervisors 108, EPs 122, Leafs 104, Controllers 116, or any other system or resource. For example, Assurance Appliance 300 can be a logical service or application running on one or more VMs 110 in Network Environment 100.

The Assurance Appliance 300 can include Data Framework 308, which can be based on, for example, APACHE APEX and HADOOP. In some cases, assurance checks can be written as individual operators that reside in Data Framework 308. This enables a natively horizontal scale-out architecture that can scale to arbitrary number of switches in Fabric 120 (e.g., ACI fabric).

Assurance Appliance 300 can poll Fabric 120 at a configurable periodicity (e.g., an epoch). The analysis workflow can be setup as a DAG (Directed Acyclic Graph) of Operators 310, where data flows from one operator to another and eventually results are generated and persisted to Database 302 for each interval (e.g., each epoch).

The north-tier implements API Server (e.g., APACHE Tomcat and Spring framework) 304 and Web Server 306. A graphical user interface (GUI) interacts via the APIs exposed to the customer. These APIs can also be used by the customer to collect data from Assurance Appliance 300 for further integration into other tools.

Operators 310 in Data Framework 308 (e.g., APEX/Hadoop) can together support assurance operations. Below are non-limiting examples of assurance operations that can be performed by Assurance Appliance 300 via Operators 310.

Security Policy Adherence

Assurance Appliance 300 can check to make sure the configurations or specification from L_Model 270A, which may reflect the user's intent for the network, including for example the security policies and customer-configured contracts, are correctly implemented and/or rendered in Li_Model 272, Ci_Model 274, and Hi_Model 276, and thus properly implemented and rendered by the fabric members (e.g., Leafs 104), and report any errors, contract violations, or irregularities found.

Static Policy Analysis

Assurance Appliance 300 can check for issues in the specification of the user's intent or intents (e.g., identify contradictory or conflicting policies in L_Model 270A).

TCAM Utilization

TCAM is a scarce resource in the fabric (e.g., Fabric 120). However, Assurance Appliance 300 can analyze the TCAM utilization by the network data (e.g., Longest Prefix Match (LPM) tables, routing tables, VLAN tables, BGP updates, etc.), Contracts, Logical Groups 118 (e.g., EPGs), Tenants, Spines 102, Leafs 104, and other dimensions in Network Environment 100 and/or objects in MIM 200, to provide a network operator or user visibility into the utilization of this scarce resource. This can greatly help for planning and other optimization purposes.

Endpoint Checks

Assurance Appliance 300 can validate that the fabric (e.g. fabric 120) has no inconsistencies in the Endpoint information registered (e.g., two leafs announcing the same endpoint, duplicate subnets, etc.), among other such checks.

Tenant Routing Checks

Assurance Appliance 300 can validate that BDs, VRFs, subnets (both internal and external), VLANs, contracts, filters, applications, EPGs, etc., are correctly programmed.

Infrastructure Routing

Assurance Appliance 300 can validate that infrastructure routing (e.g., IS-IS protocol) has no convergence issues leading to black holes, loops, flaps, and other problems.

MP-BGP Route Reflection Checks

The network fabric (e.g., Fabric 120) can interface with other external networks and provide connectivity to them via one or more protocols, such as Border Gateway Protocol (BGP), Open Shortest Path First (OSPF), etc. The learned routes are advertised within the network fabric via, for example, MP-BGP. These checks can ensure that a route reflection service via, for example, MP-BGP (e.g., from Border Leaf) does not have health issues.

Logical Lint and Real-time Change Analysis

Assurance Appliance 300 can validate rules in the specification of the network (e.g., L_Model 270A) are complete and do not have inconsistencies or other problems. MOs in the MIM 200 can be checked by Assurance Appliance 300 through syntactic and semantic checks performed on L_Model 270A and/or the associated configurations of the MOs in MIM 200. Assurance Appliance 300 can also verify that unnecessary, stale, unused or redundant configurations, such as contracts, are removed.

FIG. 3B illustrates an architectural diagram of an example system 350 for network assurance. In some cases, system 350 can correspond to the DAG of Operators 310 previously discussed with respect to FIG. 3A In this example, Topology Explorer 312 communicates with Controllers 116 (e.g., APIC controllers) in order to discover or otherwise construct a comprehensive topological view of Fabric 120 (e.g., Spines 102, Leafs 104, Controllers 116, Endpoints 122, and any other components as well as their interconnections). While various architectural components are represented in a singular, boxed fashion, it is understood that a given architectural component, such as Topology Explorer 312, can correspond to one or more individual Operators 310 and may include one or more nodes or endpoints, such as one or more servers, VMs, containers, applications, service functions (e.g., functions in a service chain or virtualized network function), etc.

Topology Explorer 312 is configured to discover nodes in Fabric 120, such as Controllers 116, Leafs 104, Spines 102, etc. Topology Explorer 312 can additionally detect a majority election performed amongst Controllers 116, and determine whether a quorum exists amongst Controllers 116. If no quorum or majority exists, Topology Explorer 312 can trigger an event and alert a user that a configuration or other error exists amongst Controllers 116 that is preventing a quorum or majority from being reached. Topology Explorer 312 can detect Leafs 104 and Spines 102 that are part of Fabric 120 and publish their corresponding out-of-band management network addresses (e.g., IP addresses) to downstream services. This can be part of the topological view that is published to the downstream services at the conclusion of Topology Explorer's 312 discovery epoch (e.g., 5 minutes, or some other specified interval).

Unified Collector 314 can receive the topological view from Topology Explorer 312 and use the topology information to collect information for network assurance from Fabric 120. Such information can include L_Model 270A and/or LR_Model 270B from Controllers 116, switch software configurations (e.g., Ci_Model 274) from Leafs 104 and/or Spines 102, hardware configurations (e.g., Hi_Model 276) from Leafs 104 and/or Spines 102, etc. Unified Collector 314 can collect Ci_Model 274 and Hi_Model 276 from individual fabric members (e.g., Leafs 104 and Spines 102).

Unified Collector 314 can poll the devices that Topology Explorer 312 discovers in order to collect data from Fabric 120 (e.g., from the constituent members of the fabric). Unified Collector 314 can collect the data using interfaces exposed by Controller 116 and/or switch software (e.g., switch OS), including, for example, a Representation State Transfer (REST) Interface and a Secure Shell (SSH) Interface.

In some cases, Unified Collector 314 collects L_Model 270A, LR_Model 270B, and/or Ci_Model 274 via a REST API, and the hardware information (e.g., configurations, tables, fabric card information, rules, routes, etc.) via SSH using utilities provided by the switch software, such as virtual shell (VSH or VSHELL) for accessing the switch command-line interface (CLI) or VSH_LC shell for accessing runtime state of the line card.

Unified Collector 314 can poll other information from Controllers 116, including: topology information, tenant forwarding/routing information, tenant security policies, contracts, interface policies, physical domain or VMM domain information, OOB (out-of-band) management IP's of nodes in the fabric, etc.

Unified Collector 314 can also poll other information from Leafs 104 and Spines 102, such as: Ci Models 274 for VLANs, BDs, security policies, Link Layer Discovery Protocol (LLDP) connectivity information of Leafs 104 and/or Spines 102, endpoint information from EPM/COOP, fabric card information from Spines 102, routing information base (RIB) tables, forwarding information base (FIB) tables from Leafs 104 and/or Spines 102, security group hardware tables (e.g., TCAM tables) from switches, etc.

Assurance Appliance 300 can run one or more instances of Unified Collector 314. For example, Assurance Appliance 300 can run one, two, three, or more instances of Unified Collector 314. The task of data collecting for each node in the topology (e.g., Fabric 120 including Spines 102, Leafs 104, Controllers 116, etc.) can be sharded or load balanced, to a unique instance of Unified Collector 314. Data collection across the nodes can thus be performed in parallel by one or more instances of Unified Collector 314. Within a given node, commands and data collection can be executed serially. Assurance Appliance 300 can control the number of threads used by each instance of Unified Collector 314 to poll data from Fabric 120.

Data collected by Unified Collector 314 can be compressed and sent to downstream services. In some examples, Unified Collector 314 can collect data in an online fashion or real-time fashion, and send the data downstream, as it is collected, for further analysis. In some examples, Unified Collector 314 can collect data in an offline fashion, and compile the data for later analysis or transmission.

Assurance Appliance 300 can contact Controllers 116, Spines 102, Leafs 104, and other nodes to collect various types of data. In some scenarios, Assurance Appliance 300 may experience a failure (e.g., connectivity problem, hardware or software error, etc.) that prevents it from being able to collect data for a period of time. Assurance Appliance 300 can handle such failures seamlessly, and generate events based on such failures.

Switch Logical Policy Generator 316 can receive L_Model 270A and/or LR_Model 270B from Unified Collector 314 and calculate Li_Model 272 for each network device i (e.g., switch i) in Fabric 120. For example, Switch Logical Policy Generator 316 can receive L_Model 270A and/or LR_Model 270B and generate Li_Model 272 by projecting a logical model for each individual node i (e.g., Spines 102 and/or Leafs 104) in Fabric 120. Switch Logical Policy Generator 316 can generate Li_Model 272 for each switch in Fabric 120, thus creating a switch logical model based on L_Model 270A for each switch.

Switch Logical Configuration Generator 316 can also perform change analysis and generate lint events or records for problems discovered in L_Model 270A and/or LR_Model 270B. The lint events or records can be used to generate alerts for a user or network operator.

Policy Operator 318 can receive Ci_Model 274 and Hi_Model 276 for each switch from Unified Collector 314, and Li_Model 272 for each switch from Switch Logical Policy Generator 316, and perform assurance checks and analysis (e.g., security adherence checks, TCAM utilization analysis, etc.) based on Ci_Model 274, Hi_Model 276, and Li_Model 272. Policy Operator 318 can perform assurance checks on a switch-by-switch basis by comparing one or more of the models.

Returning to Unified Collector 314, Unified Collector 314 can also send L_Model 270A and/or LR_Model 270B to Routing Policy Parser 320, and Ci_Model 274 and Hi_Model 276 to Routing Parser 326.

Routing Policy Parser 320 can receive L_Model 270A and/or LR_Model 270B and parse the model(s) for information that may be relevant to downstream operators, such as Endpoint Checker 322 and Tenant Routing Checker 324. Similarly, Routing Parser 326 can receive Ci_Model 274 and Hi_Model 276 and parse each model for information for downstream operators, Endpoint Checker 322 and Tenant Routing Checker 324.

After Ci_Model 274, Hi_Model 276, L_Model 270A and/or LR_Model 270B are parsed, Routing Policy Parser 320 and/or Routing Parser 326 can send cleaned-up protocol buffers (Proto Buffs) to the downstream operators, Endpoint Checker 322 and Tenant Routing Checker 324. Endpoint Checker 322 can then generate events related to Endpoint violations, such as duplicate IPs, APIPA, etc., and Tenant Routing Checker 324 can generate events related to the deployment of BDs, VRFs, subnets, routing table prefixes, etc.

FIG. 3C illustrates a schematic diagram of an example system for static policy analysis in a network (e.g., Network Environment 100). Static Policy Analyzer 360 can perform assurance checks to detect configuration violations, logical lint events, contradictory or conflicting policies, unused contracts, incomplete configurations, etc. Static Policy Analyzer 360 can check the specification of the user's intent or intents in L_Model 270A to determine if any configurations in Controllers 116 are inconsistent with the specification of the user's intent or intents.

Static Policy Analyzer 360 can include one or more of the Operators 310 executed or hosted in Assurance Appliance 300. However, in other configurations, Static Policy Analyzer 360 can run one or more operators or engines that are separate from Operators 310 and/or Assurance Appliance 300. For example, Static Policy Analyzer 360 can be a VM, a cluster of VMs, or a collection of endpoints in a service function chain.

Static Policy Analyzer 360 can receive as input L Model 270A from Logical Model Collection Process 366 and Rules 368 defined for each feature (e.g., object) in L_Model 270A. Rules 368 can be based on objects, relationships, definitions, configurations, and any other features in MIM 200. Rules 368 can specify conditions, relationships, parameters, and/or any other information for identifying configuration violations or issues.

Moreover, Rules 368 can include information for identifying syntactic violations or issues. For example, Rules 368 can include one or more rules for performing syntactic checks. Syntactic checks can verify that the configuration of L_Model 270A is complete, and can help identify configurations or rules that are not being used. Syntactic checks can also verify that the configurations in the hierarchical MIM 200 are complete (have been defined) and identify any configurations that are defined but not used. To illustrate, Rules 368 can specify that every tenant in L_Model 270A should have a context configured; every contract in L_Model 270A should specify a provider EPG and a consumer EPG; every contract in L_Model 270A should specify a subject, filter, and/or port; etc.

Rules 368 can also include rules for performing semantic checks and identifying semantic violations or issues. Semantic checks can check conflicting rules or configurations. For example, Rule1 and Rule2 can have aliasing issues, Rule1 can be more specific than Rule2 and thereby create conflicts/issues, etc. Rules 368 can define conditions which may result in aliased rules, conflicting rules, etc. To illustrate, Rules 368 can specify that an allow policy for a specific communication between two objects can conflict with a deny policy for the same communication between two objects if they allow policy has a higher priority than the deny policy, or a rule for an object renders another rule unnecessary.

Static Policy Analyzer 360 can apply Rules 368 to L_Model 270A to check configurations in L_Model 270A and output Configuration Violation Events 370 (e.g., alerts, logs, notifications, etc.) based on any issues detected. Configuration Violation Events 370 can include semantic or semantic problems, such as incomplete configurations, conflicting configurations, aliased rules, unused configurations, errors, policy violations, misconfigured objects, incomplete configurations, incorrect contract scopes, improper object relationships, etc.

In some cases, Static Policy Analyzer 360 can iteratively traverse each node in a tree generated based on L_Model 270A and/or MIM 200, and apply Rules 368 at each node in the tree to determine if any nodes yield a violation (e.g., incomplete configuration, improper configuration, unused configuration, etc.). Static Policy Analyzer 360 can output Configuration Violation Events 370 when it detects any violations.

FIG. 4 illustrates a flowchart for an example network assurance method. The method shown in FIG. 4 is provided by way of example, as there are a variety of ways to carry out the method. Additionally, while the example method is illustrated with a particular order of blocks, those of ordinary skill in the art will appreciate that FIG. 4 and the blocks shown therein can be executed in any order and can include fewer or more blocks than illustrated.

Each block shown in FIG. 4 represents one or more steps, processes, methods or routines in the method. For the sake of clarity and explanation purposes, the blocks in FIG. 4 are described with reference to Assurance Appliance 300, Models 270A-B, 272, 274, 276, and Network Environment 100, as shown in FIGS. 1A-B, 2D, and 3A.

At step 400, Assurance Appliance 300 can collect data and obtain models associated with Network Environment 100. The models can include Models 270A-B, 272, 274, 276. The data can include fabric data (e.g., topology, switch, interface policies, application policies, EPGs, etc.), network configurations (e.g., BDs, VRFs, L2 Outs, L3 Outs, protocol configurations, etc.), security configurations (e.g., contracts, filters, etc.), service chaining configurations, routing configurations, and so forth. Other information collected or obtained can include, for example, network data (e.g., RIB/FIB, VLAN, MAC, ISIS, DB, BGP, OSPF, ARP, VPC, LLDP, MTU, QoS, etc.), rules and tables (e.g., TCAM rules, ECMP tables, etc.), endpoint dynamics (e.g., EPM, COOP EP DB, etc.), statistics (e.g., TCAM rule hits, interface counters, bandwidth, etc.).

At step 402, Assurance Appliance 300 can analyze and model the received data and models. For example, Assurance Appliance 300 can perform formal modeling and analysis, which can involve determining equivalency between models, including configurations, policies, etc.

At step 404, Assurance Appliance 300 can generate one or more smart events. Assurance Appliance 300 can generate smart events using deep object hierarchy for detailed analysis, such as Tenants, switches, VRFs, rules, filters, routes, prefixes, ports, contracts, subjects, etc.

At step 406, Assurance Appliance 300 can visualize the smart events, analysis and/or models. Assurance Appliance 300 can display problems and alerts for analysis and debugging, in a user-friendly GUI.

FIG. 5 illustrates an example event clustering system 500. The event clustering system 500 can cluster network events for purposes of providing network assurance. For example, the event clustering system can cluster network events and subsequently generate confidence scores from the clustered network events for purposes of providing network assurance.

The event clustering system 500 can be implemented at one or a plurality of devices in a network environment. For example, the event clustering system 500 can be implemented across one or a plurality of network devices, leafs, spines, controllers, and servers. In another example, the event cluster system 500 can be distributed across a plurality of routers in a network fabric.

The event clustering system 500 can be implemented through one or a combination of an appliance, application, and virtual machine. For example, the event clustering system 500 can be implemented as part of the assurance appliance 300.

The event clustering system 500 includes a network event sorter 502, event group storage 504, a network event analyzer 506, a network event user interface 508, and a minimum support event filter 510.

The network event sorter 502 can receive and sort network events. Network events can include events related to operation of a network environment, such as the events described herein. For example, the network event sorter 502 can receive a network event occurring within a specific logical tenant space on a specific router indicating the router has failed a policy test. The network event sorter 502 can receive a plurality of network events generated at an epoch, e.g. each epoch of a configurable periodicity. Network events received by the network event sorter 502 can be generated, collected, or otherwise identified by a controller, application, or appliance, e.g. assurance appliance 300.

A network event can indicate an event state associated with the network event. An event state indicates a grade or subject associated with a network event. For example, an event state of a network event can indicate whether the network event is a passing event, a failing event, an informational event, or an error event.

Network events can be generated in response to checks performed in a network environment, e.g. at a configurable periodicity. For example, a network event can be generated in response to performing a policy check on an EPG in a network environment. Additionally, network events can be generated based on operation of a network environment. For example, a network event can indicate an error occurred during operation of a network environment within a specific logical location in a tenant space in the network environment. A network event can include can include one or a combination of values of parameters defining the network event, an event state associated with the network event, a generated error or warning, a log, and stimuli and circumstances that led to creation of the network event.

Network events and indicated event states can be associated with or otherwise classified by event categories. Event categories can be defined according to characteristics of a network environment in operation causing creation of corresponding network events. Examples of event categories include policy events, tenant routing events, configuration violation events, logical lint events, and forwarding events. For example, if a failure event is created in response to a bridge domain failing to adhere to a policy during a policy check, then the event can be classified as a failed policy event. In another example, if a network device in a tenant logical space passes a forwarding or routing check, then the event can be characterized as a passing forwarding event.

Either or both event states and event categories can be indicated by specific event codes associated with network events. For example, a network event can be associated with a failing event code indicating the event is a failing event. In another example, a network event can be associated with a passing forwarding event code indicating the event is a passing forwarding event, e.g. the event is created in response to a passed forwarding test and/or data is being forward through a logical portion of a network environment.

A network event can be defined by values of parameters of a network environment. Parameters of a network environment include parameters that define characteristics of a network environment through creation and maintenance of the network environment. For example, parameters of a network environment can include a model, e.g. a logical model, which can be deployed into a network fabric forming a network environment. Specifically, parameters of a network environment can include parameters that define a network environment on one or a combination of a network layer, a logical layer, and a physical layer.

Parameters that define a network environment on a network layer can include network hierarchy parameters that form a hierarchy of network parameters. Specifically, parameters of a network environment can include an identification of a tenant, e.g. in a network layer, a bridge domain, and a virtual routing and forwarding instance. For example, values of parameters defining a network environment on a network layer can include a specific VRF of a specific tenant in which a network event associated with a forwarding failure occurs.

Parameters defining a network environment on a logical layer can include logical hierarchy parameters that form a hierarchy of logical parameters. Specifically, parameters of a network environment can include an identification of a tenant, e.g. in a local layer, an application profile, and an identification of an endpoint group. For example, values of parameters defining a network environment on a logical layer can include a specific application executing for a specific endpoint group in which a network event associated with a policy failure occurs.

Parameters defining a network environment on a physical layer can include physical hierarchy parameters that form a hierarchy of physical parameters. Specifically, parameters of a network environment can include characteristics of a device, an identification of a network device, and characteristics of a physical port. For example, values of parameters defining a network environment on a physical layer can include a specific port used to transmit data about which a network event is created, and an indication of whether a physical device which includes the port is a spine router or a leaf router.

The network event sorter 502 can sort received network events into event groups. In sorting network events into event groups, the network event sorter 502 can add a network event into one or a plurality of event groups. For example, the network event sorter 502 can sort a policy failure event occurring in a specific EPG into an event group for policy failures and also an event group for the specific EPG.

The network event sorter 502 can sort network events into event groups based on parameters of a network environment defining either or both the network events and the event groups. For example, if a network event occurs in a specific bridge domain in a specific tenant logical space, then the network event sorter 502 can sort the network event into an event group defined for the specific bridge domain in the specific tenant logical space. In another example, if a network event occurs at a first port on a specific leaf router, then the network event sorter 502 can sort the network event into an event group defined for the specific leaf router.

The network event sorter 502 can sort received network events into event groups based on event categories associated with the network events. For example, if a network event is a tenant routing event failure, then the network event sorter 502 can sort the network event into an event group including tenant routing events. Additionally, the network event sorter 502 can sort received network events into event groups based on both parameters of a network environment and event categories defining the network events and event groups. For example, if a network event is a policy failure event occurring at a specific EPG in a specific tenant space, then the network event sorter 502 can sort the network event into an event group including policy failures for the specific tenant.

Referring back to the event clustering system 500 shown in FIG. 5, the event group storage 504 stores data indicating defined event groups and network events sorted into the event groups by the network event sorter 502. Specifically, the network event sorter 502 can update the event group storage 504 to include defined event groups and network events sorted into the event groups. For example, the network event sorter 502 can update the event group storage 504 to indicate a created event group for network events occurring at a specific leaf router in a specific tenant space. Further in the example, the network event sorter 502 can update the event group storage 504 to include network events occurring at the specific leaf router in the tenant space that are sorted into the event group.

The network event analyzer 506 can analyze events sorted by the network event sorter 502, potentially for purposes of providing network assurance. Specifically, the network event analyzer 506 can analyze events sorted into one or a plurality of event groups by the network event sorter 502, as indicated by data stored in the event group storage 504. For example, the network event analyzer 506 can analyze network events sorted into an event group defined for a specific application executing in a specific EPG of a tenant.

In analyzing network events, the network event analyzer 506 can identify either or both event states and event categories of the network events. For example, the network event analyzer 506 can identify a network event is a tenant routing failure event. The network event analyzer 506 can identify either or both event states and event categories of network events based upon event groups of the network events. For example, if a network event is sorted into an event group of failed policy events, then the network event analyzer 506 can determine the event is or is otherwise related to a failed policy event.

Additionally, in analyzing network events, the network event analyzer 506 can identify values of parameters of a network environment defining the network events. For example, the network event analyzer 506 can identify a network event was generated in response to operation of a VRF in a specific tenant space. The network event analyzer 506 can identify values of parameters of a network environment for network events based on event groups of the network events. For example, if a network event is sorted into an event group defined by a specific bridge domain and a specific leaf router, then the network event analyzer 506 can determine the event was created through operation of the specific leaf router in the specific bridge domain.

The network event analyzer 506 can identify a confidence score that one or a combination of values of parameters of a network environment is associated with a specific event state. For example, the network event analyzer 506 can identify a confidence score indicating there is a 95% chance a port of a spine router is associated with a failing network event, e.g. a failing network event is generated through operation of the port. Further, the network event analyzer can identify a confidence score that one or a combination of values of parameters of a network environment is associated with a specific event state and an event category. For example, the network event analyzer 506 can identify a confidence score indicating there is a 92% chance an EPG is associated with a passing policy event.

A confidence score identified by the network event analyzer 506 can indicate a frequency at which network events defined by one or a combination of values of parameters of a network environment have either or both a specific event state and a specific event category. The network event analyzer 506 can use one or a combination of identified event states of network events, identified event categories of network events, event groups of network events, and identified values of parameters of a network environment defining network events to identify a confidence score. For example, the network event analyzer 506 can identify there is a 99% chance a leaf router is associated with a passing forwarding event from network events sorted into an event group for the leaf router.

In various embodiments, the network event analyzer 506 can maintain a plurality of Gaussian distributions for identified confidence scores. More specifically, the network event analyzer 506 can continually update a Gaussian distribution of confidence scores after the confidence scores are identified each epoch. A Gaussian distribution of confidence scores maintained by the network event analyzer 506 can be specific to one or a combination of values of parameters defining a network environment. For example, the network event analyzer 506 can maintain a Gaussian distribution of confidence scores maintained for a certain EPG of a tenant. Additionally, a Gaussian distribution of confidence scores maintained by the network event analyzer 506 can be specific to either or both an event state and an event category. For example the network event analyzer 506 can maintain a Gaussian distribution of confidence scores that a specific leaf router is associated with a failed policy check.

The network event analyzer 506 can present an analysis of sorted network events to a user, e.g. a network administrator, through the network event user interface 508. As part of presenting an analysis of sorted network events to a user through the network event user interface 508, the network event analyzer 506 can present one or a combination of an identified confidence score, an event group of a network event, an identification of one or a combination of parameters of a network environment defining one or a plurality of network events, an event state of one or a plurality of network events, and an event category of a network event. For example, the network event analyzer 506 can present to a user that there is a 94% chance a specific application executing in an EPG is associated with a failed policy event. In presenting an analysis of network events, e.g. an identified confidence score, to a user, the network event analyzer 506 can reduce or eliminate the burden on the user of having to sort through thousands of network events in providing network assurance.

The network event analyzer 506 can present an analysis of sorted network events based on received user input, e.g. input received through the network event user interface 508. For example, the network event analyzer 506 can present a confidence score that a specific value of a parameter defining a network environment is associated with a failing event. In response to presenting the confidence score to a user, the network event analyzer 506 can receive input requesting a breakdown of the confidence score into specific event categories of failing events. In response to the input, the network event analyzer 506 can present confidence scores that the value is associated with the specific event categories of failing events.

Additionally, the network event analyzer 506 can present an analysis of sorted network events based on a determined confidence score. Specifically, the network event analyzer 506 can present one or a combination of a confidence score that a value of a parameter of a network environment is associated with a specific event state, an identification of the value of the parameter, and an identification of the event state, if the confidence score is above a threshold score. For example, if a confidence score that a specific EPG is associated with a failing event, then the network event analyzer 506 can present the confidence score indicating a percentage that the EPG is associated with a failing event.

Referring back to the event clustering system 500 shown in FIG. 5, the minimum support event filter 510 filters network events for purposes of the network event analyzer 506 analyzing the filtered network events. The minimum support event filter 510 can filter network events sorted into event groups. For example, the minimum support event filter 510 can filter events sorted into an event group defined to include network events occurring at a specific leaf router. The network event analyzer 506 can analyze network events after the events are filtered by the minimum support event filter 510.

The minimum support event filter 510 can use a minimum support, e.g. a value, to filter out network events. A minimum support can specify a threshold number of times a network event appears in a set of network events, network events with a specific event state appear in a set of network events, network events in a specific event category appear in a set of network events, and/or values of parameters defining network events appear in a set of network events in order to analyze the network event or network events. For example, a minimum support can specify one thousand events, and if only two events occurring in a specific bridge domain appear in an epoch, then the minimum support event filter 510 can filter out the events from a plurality of network events generated during the epoch. In filtering network events, the minimum support event filter 510 can identify whether network events, an event state of network events, an event category of network events, and values of parameters defining network events meets the minimum support. A minimum support used by the minimum support event filter 510 can be set by a user. By using a minimum support to filter network events before analyzing network events, computational resources are conserved. Further, using a minimum support to filter network events before analyzing the events decreases or eliminates creation of unwanted or useless data, data that can potentially confuse an administrator or waste the administrator's time.

FIG. 6 illustrates a flowchart for an example network assurance analysis method. The method shown in FIG. 6 is provided by way of example, as there are a variety of ways to carry out the method. Additionally, while the example method is illustrated with a particular order of blocks, those of ordinary skill in the art will appreciate that FIG. 6 and the blocks shown therein can be executed in any order and can include fewer or more blocks than illustrated.

Each block shown in FIG. 6 represents one or more steps, processes, methods or routines in the method. For the sake of clarity and explanation purposes, the blocks in FIG. 6 are described with reference to the event clustering system 500 shown in FIG. 5.

At step 600, the network event sorter 502 sorts received network events based on values of parameters of a network environment defining the network events. Network events can be received from and generated or identified by one or a combination of a controller, application, or appliance, e.g. assurance appliance 300. Network events can also be sorted at step 600 based on either or both an event state of the network events and event categories of the network events.

At step 602, the network event analyzer 506 identifies event states of the network events. Event groups the network events are sorted into can be used to identify event states of the network events. For example, if a network event is sorted into an event group of failing policy events, then the network event analyzer 506 can identify an event state of the network event is a failing event.

At step 604, the network event analyzer 506 determines a confidence score that at least one of the values of the parameters of the network environment is associated with a specific event state of the identified event states. A confidence score can be calculated by comparing a number of events at a specific event state defined by the at least one of the values of the parameters of the network environment with a total number of events defined by the at least one of the values of the parameters of the network environment.

At step 606, the network event analyzer 506 maintains, over time, a Gaussian distribution of the confidence scores for the at least one of the values of the parameters. A Gaussian distribution can be maintained using confidence scores determined for the at least one of the values of the parameters during each epoch or another configurable periodicity. As will be discussed in further detail later, a Gaussian distribution of the confidence scores can be used to identify anomalies in the network environment.

As network environments grow in complexity and size detecting anomalies in the network environments becomes increasingly difficult. In particular, as network environments grow in size and complexity, leading to creation of greater amounts of network events, it becomes more difficult to identify anomalies in the network environments based on the network events. Monitoring confidence scores for values of parameters of a network environment defining network events in the environment can be used to efficiently and accurately detect anomalies in the environment regardless of a large number of events occurring in the network environment.

Further, usually a state of a network environment or a portion of a network environment changes quickly after an anomaly occurs in the environment. This makes identifying a relevant network state of the network environment after detecting an anomaly difficult, potentially leading to problems with diagnosing problems and fixing the problems that are associated with or otherwise caused the anomaly. Additionally, anomalies often occur while a user is not actively monitoring a network environment further exacerbating issues with diagnosing and fixing problems in a network environment that are associated with the anomalies. A relevant state of a network environment can be automatically determined after detection of an anomaly in the environment to allow a user to diagnose and fix problems, even if the user is not actively monitoring the environment when the anomaly occurs, e.g. the user is offline.

FIG. 7 illustrates an example anomaly detection and reporting system 700. The anomaly detection and reporting system 700 can detect an anomaly occurring in a network environment. Additionally, the anomaly detection and reporting system 700 can identify a relevant network state of a network environment or a portion of the network environment in response to detection of an anomaly in the network environment. The anomaly detection and reporting system 700 can present either or both a detected anomaly and a relevant network state of a network environment associated with the anomaly to a user, potentially for purposes of providing network assurance.

The anomaly detection and reporting system 700 can be implemented at one or a plurality of devices in a network environment. For example, the anomaly detection and reporting system 700 can be implemented across one or a plurality of network devices, leafs, spines, controllers, and servers. In another example, the anomaly detection and reporting system 700 can be distributed across a plurality of routers in a network fabric.

The anomaly detection and reporting system 700 can be implemented through one or a combination of an appliance, application, and virtual machine. For example, the anomaly detection and reporting system 700 can be implemented as part of the assurance appliance 300.

The anomaly detection and reporting system 700 includes an anomaly detector 702, an anomaly storage 704, a relevant network state identifier 706, a relevant network state storage 708, and an anomaly reporting user interface 710.

The anomaly detector 702 can detect anomalies occurring in a network environment. Anomalies occurring in a network environment can include behaviors that deviate from or are otherwise different from expected or normal behaviors in a network environment. For example, an anomaly can include a device switching or otherwise becoming associated with a new EPG. In another example, an anomaly can include a leaf router failing to adhere to a policy. Anomalies can occur in the operation of a network environment and/or in response to checks or tests performed in the network environment. For example, anomalies can occur or be identified in response to network assurance checks performed in a network environment.

Network events generated for a network environment can be used by the anomaly detector 702 to detect anomalies in a network environment. More specifically, the anomaly detector 702 can detect anomalies in a network environment based on confidence scores determined using network events generated for the network environment. For example, the anomaly detector 702 can analyze confidence scores that a specific EPG is associated with a failed forwarding check to detect occurrence of an anomaly in the EPG. The anomaly detector 702 can compare a plurality of determined confidence scores to detect anomalies in a network environment. For example, the anomaly detector 702 can track changes to a confidence score for a tenant to determine whether the confidence score deviates from an average confidence score great than a threshold amount. Further in the example, if the confidence score deviates from the average confidence score greater than the threshold amount, the anomaly detector 702 can identify that an anomaly has actually occurred.

The anomaly detector 702 can automatically detect anomalies in a network environment. More specifically, the anomaly detector 702 can use network events, as they are generated, to identify anomalies in a network environment. For example, the anomaly detector 702 can automatically determine if anomalies exist in a network environment after each epoch, or configurable periodicity, using corresponding network events generated during each epoch. In automatically detecting anomalies in a network environment, the anomaly detector 702 can detect the anomalies, e.g. absent user input or instruction. For example, the anomaly detector 702 can automatically detect anomalies in a network environment when a user is offline or otherwise not actively monitoring the network environment and providing instructions or input to the anomaly detector 702. By automatically detecting anomalies, the anomaly detector 702 can decrease burdens on a network administrator in providing network assurance. More specifically, a network administrator no longer has to parse through a multitude of network events to identify anomalies in a network environment, as the anomalies can be automatically identified for the administrator.

The anomaly detector 702 can detect anomalies using a Gaussian distribution generated for network events. In using a Gaussian distribution to detect anomalies, the anomaly detector 702 can use either or both a variance and a mean of the Gaussian distribution of a confidence score to identify anomalies in a network environment. For example, if a current confidence score falls out of a threshold variance, e.g. three sigma, of a Gaussian distribution, then the anomaly detector 702 can determine an anomaly has occurred. In another example, if a confidence score falls out of a threshold mean for Gaussian distribution, then the anomaly detector 702 can determine an anomaly has occurred. In yet another example, if a mean confidence score of a Gaussian distribution begins to migrate away from a previous mean of the Gaussian distribution, then the anomaly detection 702 can determine an anomaly has occurred.

The anomaly detector 702 can simultaneously monitor a plurality of Gaussian distributions, corresponding to different values of parameters defining a network environment and combinations of the values of the parameters, to detect anomalies in the network environment. For example, the anomaly detector 702 can monitor Gaussian distributions of confidence scores that a plurality of EPGs of a tenant pass a policy check to determine when one of the EPGs continues to fail a policy check, e.g. an anomaly.

In identifying an anomaly, the anomaly detector 702 can identify and otherwise associate one or a combination of values of parameters defining a network environment with an anomaly. Specifically, the anomaly detector 702 can associate values of parameters defining network events and subsequent confidence scores used in detecting an anomaly with the actual anomaly. For example, if confidence scores for events occurring at a specific leaf router are used to detect an anomaly occurring at the leaf router, then the anomaly detector 702 can associate an identification of the leaf router with the anomaly. In another example, if confidence scores that a specific EPG is passing security adherence checks are used to detect an anomaly, then the anomaly detector 702 can associate the EPG with the anomaly, e.g. an anomaly of failing security adherence checks.

The anomaly detector 702 can identify or otherwise indicate a time an anomaly occurs or is detected. A time an anomaly occurs can include or be based on a time an anomaly is detected in a network environment. For example, a time an anomaly occurs can include a specific epoch in which the anomaly was detected. In another example, a time an anomaly occurs can include a time when network events used in detecting the anomaly actually occurred. In yet another example, a time an anomaly occurs can include an epoch in which network events used in detecting the anomaly occurred.

Returning to the example anomaly detection and reporting system 700, shown in FIG. 7, the anomaly storage 704 can store data indicating detected anomalies. Data stored in the anomaly storage 704 can indicate one or a combination of an identification of an anomaly, a time an anomaly occurred or was detected, values of parameters of a network environment associated with an anomaly, characteristics of a detected anomaly, confidence scores used in identifying an anomaly, and values of parameters of a network environment defining network events used in detecting an anomaly. For example, if confidence scores created from network events occurring in a specific bridge domain are used in detecting an anomaly, then data stored in the anomaly storage 704 can indicate the anomaly is associated with the specific bridge domain. Data stored in the anomaly storage 704 can be maintained by the anomaly detector 702. For example, the anomaly detector 702 can update data stored in the anomaly storage 704 after the anomaly detector 702 actually detects an anomaly.

The relevant network state identifier 706 can identify a relevant network state of a network environment. Additionally, the relevant network state identifier 706 can identify a relevant network state of a network environment using data stored in the anomaly storage 704. A relevant network state of a network environment can include one or a combination of configurations, policies, deployed models, fabric data, etc. of all or a portion of the network environment. For example, a relevant network state of a network environment can include policies for an EPG at a specific time. In another example, a relevant network state can include an identification of new peers learned or discovered.

The relevant network state identifier 706 can identify a relevant network state of a network environment in response to detection of an anomaly. Specifically, after the anomaly detector 702 detects an anomaly in a network environment, the relevant network state identifier 706 can determine a relevant network state of the network environment. The relevant network state identifier 706 can identify a relevant network state of a network environment within a specific amount of time after either or both detection of an anomaly and an actual occurrence of the anomaly in the environment. For example, the relevant network state identifier 706 can identify a relevant network state of a network environment within five minutes of an occurrence of an anomaly in the environment. In identifying a relevant network state in response to detection of an anomaly, and potentially within a specific time frame after an anomaly occurs and/or the anomaly is detected, the chances that a network state of the environment changes or is otherwise lost are reduced. This helps to ensure a relevant network state can actually be identified to allow a network administrator to diagnose and fix problems in the network environment.

The relevant network state identifier 706 can identify a relevant network state of a network environment when a user is offline or is otherwise not monitoring the network environment. For example, if an anomaly occurs in a network environment in the night while an administrator is sleeping, the relevant network state identifier 706 can still identify a relevant network state of the environment. The identified relevant network state can subsequently be presented to the administrator once the administrator comes online, thereby allowing the administrator to diagnose and fix problems within the network environment in order to provide network assurance.

A relevant network state of a network environment identified by the relevant network state identifier 706 can be associated with an anomaly. In being associated with an anomaly, a relevant network state of a network environment can be a state of all or a portion of the network environment at a time the anomaly occurred or at a time in proximity to an occurrence of the anomaly. For example, a state of a network environment within five minutes of an occurrence of an anomaly in the environment can be associated with the anomaly. Additionally, in being associated with an anomaly, a relevant network state of the network environment can be a state of a portion of the network environment in which the anomaly is detected. For example, if an anomaly occurs within a specific bridge domain of a tenant, then the bridge domain can be associated with the anomaly.

The relevant network state identifier 706 can identify a relevant network state from data collected from or generated by either or both a network assurance appliance/agent and a controller. For example, the relevant network state identifier 706 can collect, from an APIC controller, statistics of a specific tenant or specific EPG in response to detecting an anomaly in the EPG. In another example, the relevant network state identifier 706 can collect, from a leaf agent, traffic statistics of ports of a leaf router where an anomaly is detected.

The relevant network state identifier 706 can query either or both an appliance/agent and a controller for data used in determining or forming part of a relevant state. For example, the relevant network state identifier 706 can query an appliance/agent for an identification of all ports on a network device that changed state within the last five minutes. In another example, the relevant network state identifier 706 can query an appliance/agent or controller for all routes learned in the last five minutes. In yet another example, the relevant network state identifier 706 can query an appliance/agent to identify if a new peer was discovered or learned during the last epoch.

A relevant network state can be identified by the relevant network state identifier 706 based on values of parameters defining network events used in detecting an anomaly, as indicated by data stored in the anomaly storage 704. For example, the relevant network state identifier 706 can gather statistics for a specific logical location in which an anomaly occurs as part of identifying a relevant network state associated with the anomaly. The relevant network state identifier 706 can use values of parameters defining network events to identify either or both a specific appliance/agent and controller to query and gather data from, in order to determine a relevant network state. For example, if an anomaly is detected at a specific spine router, then the relevant network state identifier 706 can determine to query a specific controller of the router to identify a relevant network state. Further in the example, the relevant network state identifier 706 can actually query the identified controller for an indication of all ports of the router that changed state within the last five minutes, as part of identifying the relevant network state.

The relevant network state identifier 706 can identify a relevant network state based on either or both a time an anomaly occurs and a time an anomaly is detected, as indicated by data stored in the anomaly storage 704. For example, the relevant network state identifier 706 can gather data including all routes learned during the five minutes before an anomaly occurred. In yet another example, the relevant network state identifier 706 can gather data including all new peers discovered or learned five minutes before and after an anomaly is detected.

The relevant network state identifier 706 can correlate an identified relevant network state with an anomaly. More specifically, the relevant network state identifier 706 can correlate a relevant network state, determined in response to an occurrence of an anomaly, with the anomaly itself. By correlating a relevant network state with an anomaly, a network administrator can recognize patterns in network states and corresponding anomalies, thereby potentially allowing the administrator to more quickly diagnose and fix problems in a network environment.

Returning to the example anomaly detection and reporting system 700, shown in FIG. 7, the relevant network state storage 708 can store data indicating an identified relevant network state. More specifically, the relevant network state storage 708 can store data indicating a relevant network state identified in response to detection of an anomaly in a network environment. Data stored in the relevant network state storage 708 can be maintained by the relevant network state identifier 706.

The relevant network state identifier 706 can control presentation of either or both a detected anomaly and a relevant network state to a user. Specifically, the relevant network state identifier 706 can control presentation of either or both a detected anomaly and a relevant network state to a user through the anomaly reporting user interface 710. For example, the relevant network state identifier 706 can control presentation of a relevant network state identified in response to an anomaly and values of parameters defining network events used to detect the anomaly to a user. Using presented anomalies and relevant network states, a user can quickly diagnose and fix problems within a network environment.

The relevant network state identifier 706 can control presentation of either or both a detected anomaly and a relevant network state to a user in response to detection of a present of the user. For example, the relevant network state identifier 706 can present anomalies and corresponding relevant network states to a user through the anomaly reporting user interface 710, once the user comes online.

FIG. 8 illustrates a flowchart for an anomaly detection and reporting method. The method shown in FIG. 8 is provided by way of example, as there are a variety of ways to carry out the method. Additionally, while the example method is illustrated with a particular order of blocks, those of ordinary skill in the art will appreciate that FIG. 8 and the blocks shown therein can be executed in any order and can include fewer or more blocks than illustrated.

Each block shown in FIG. 8 represents one or more steps, processes, methods or routines in the method. For the sake of clarity and explanation purposes, the blocks in FIG. 8 are described with reference to the event clustering system 500 shown in FIG. 5 and the anomaly detection and reporting system shown in FIG. 7.

At step 800, the network event analyzer 506 determines confidence scores for at least one value of parameters of a network environment defining network events occurring in the network environment. Confidence scores, defined at step 800, can indicate a frequency that network events defined by the at least one values of parameters of the network environment have a specific event state. Further, the confidence scores can be maintained as part of a Gaussian distribution of the confidence scores for the at least one value of parameters of a network environment.

At step 802, the anomaly detector 702 monitors the confidence scores to detect an anomaly in the network environment. An anomaly in the network environment can be detected by comparing a current confidence score with past confidence scores of the confidence scores. Additionally, an anomaly can be detected in the network environment can be detected by analyzing a Gaussian distribution of the confidence scores. For example, an occurrence of an anomaly can be identified if a current confidence score falls outside of a threshold variance of a Gaussian distribution of the confidence scores.

At step 804, the relevant network state identifier 706 identifies a relevant network state of the network environment in response to detecting the anomaly in the network environment. A relevant network state of the network environment can be identified based on the anomaly in the network environment. For example, using the at least one value of the parameters defining the network events used to detect the anomaly, data can be gathered from a specific controller to determine a relevant network state. In another example, data for a relevant network state can be gathered based on either or both a time the anomaly occurred in the network environment and a time the anomaly was detected.

At step 806, the anomaly reporting user interface 710 presents the relevant network state and the anomaly to a user. The relevant network state and the anomaly can be presented to a user to allow the user to diagnose and fix problems in the network environment as part of providing network assurance. Additionally, the anomaly can occur while a user is offline, e.g. not monitoring the network environment, and once the user comes online, the anomaly and the relevant network state associated with the anomaly can be presented to the user.

The disclosure now turns to FIGS. 9 and 10, which illustrate example network devices and computing devices, such as switches, routers, load balancers, client devices, and so forth.

FIG. 9 illustrates an example network device 900 suitable for performing switching, routing, load balancing, and other networking operations. Network device 900 includes a central processing unit (CPU) 904, interfaces 902, and a bus 910 (e.g., a PCI bus). When acting under the control of appropriate software or firmware, the CPU 904 is responsible for executing packet management, error detection, and/or routing functions. The CPU 904 preferably accomplishes all these functions under the control of software including an operating system and any appropriate applications software. CPU 904 may include one or more processors 908, such as a processor from the INTEL X86 family of microprocessors. In some cases, processor 908 can be specially designed hardware for controlling the operations of network device 900. In some cases, a memory 906 (e.g., non-volatile RAM, ROM, etc.) also forms part of CPU 904. However, there are many different ways in which memory could be coupled to the system.

The interfaces 902 are typically provided as modular interface cards (sometimes referred to as “line cards”). Generally, they control the sending and receiving of data packets over the network and sometimes support other peripherals used with the network device 900. Among the interfaces that may be provided are Ethernet interfaces, frame relay interfaces, cable interfaces, DSL interfaces, token ring interfaces, and the like. In addition, various very high-speed interfaces may be provided such as fast token ring interfaces, wireless interfaces, Ethernet interfaces, Gigabit Ethernet interfaces, ATM interfaces, HSSI interfaces, POS interfaces, FDDI interfaces, WIFI interfaces, 3G/4G/5G cellular interfaces, CAN BUS, LoRA, and the like. Generally, these interfaces may include ports appropriate for communication with the appropriate media. In some cases, they may also include an independent processor and, in some instances, volatile RAM. The independent processors may control such communications intensive tasks as packet switching, media control, signal processing, crypto processing, and management. By providing separate processors for the communications intensive tasks, these interfaces allow the master microprocessor 604 to efficiently perform routing computations, network diagnostics, security functions, etc.

Although the system shown in FIG. 9 is one specific network device of the present invention, it is by no means the only network device architecture on which the present invention can be implemented. For example, an architecture having a single processor that handles communications as well as routing computations, etc., is often used. Further, other types of interfaces and media could also be used with the network device 900.

Regardless of the network device's configuration, it may employ one or more memories or memory modules (including memory 906) configured to store program instructions for the general-purpose network operations and mechanisms for roaming, route optimization and routing functions described herein. The program instructions may control the operation of an operating system and/or one or more applications, for example. The memory or memories may also be configured to store tables such as mobility binding, registration, and association tables, etc. Memory 906 could also hold various software containers and virtualized execution environments and data.

The network device 900 can also include an application-specific integrated circuit (ASIC), which can be configured to perform routing and/or switching operations. The ASIC can communicate with other components in the network device 900 via the bus 910, to exchange data and signals and coordinate various types of operations by the network device 900, such as routing, switching, and/or data storage operations, for example.

FIG. 10 illustrates a computing system architecture 1000 wherein the components of the system are in electrical communication with each other using a connection 1005, such as a bus. Exemplary system 1000 includes a processing unit (CPU or processor) 1010 and a system connection 1005 that couples various system components including the system memory 1015, such as read only memory (ROM) 1020 and random access memory (RAM) 1025, to the processor 1010. The system 1000 can include a cache of high-speed memory connected directly with, in close proximity to, or integrated as part of the processor 1010. The system 1000 can copy data from the memory 1015 and/or the storage device 1030 to the cache 1012 for quick access by the processor 1010. In this way, the cache can provide a performance boost that avoids processor 1010 delays while waiting for data. These and other modules can control or be configured to control the processor 1010 to perform various actions. Other system memory 1015 may be available for use as well. The memory 1015 can include multiple different types of memory with different performance characteristics. The processor 1010 can include any general purpose processor and a hardware or software service, such as service 1 1032, service 2 1034, and service 3 1036 stored in storage device 1030, configured to control the processor 1010 as well as a special-purpose processor where software instructions are incorporated into the actual processor design. The processor 1010 may be a completely self-contained computing system, containing multiple cores or processors, a bus, memory controller, cache, etc. A multi-core processor may be symmetric or asymmetric.

To enable user interaction with the computing device 1000, an input device 1045 can represent any number of input mechanisms, such as a microphone for speech, a touch-sensitive screen for gesture or graphical input, keyboard, mouse, motion input, speech and so forth. An output device 1035 can also be one or more of a number of output mechanisms known to those of skill in the art. In some instances, multimodal systems can enable a user to provide multiple types of input to communicate with the computing device 1000. The communications interface 1040 can generally govern and manage the user input and system output. There is no restriction on operating on any particular hardware arrangement and therefore the basic features here may easily be substituted for improved hardware or firmware arrangements as they are developed.

Storage device 1030 is a non-volatile memory and can be a hard disk or other types of computer readable media which can store data that are accessible by a computer, such as magnetic cassettes, flash memory cards, solid state memory devices, digital versatile disks, cartridges, random access memories (RAMs) 1025, read only memory (ROM) 1020, and hybrids thereof.

The storage device 1030 can include services 1032, 1034, 1036 for controlling the processor 1010. Other hardware or software modules are contemplated. The storage device 1030 can be connected to the system connection 1005. In one aspect, a hardware module that performs a particular function can include the software component stored in a computer-readable medium in connection with the necessary hardware components, such as the processor 1010, connection 1005, output device 1035, and so forth, to carry out the function.

For clarity of explanation, in some instances the present technology may be presented as including individual functional blocks including functional blocks comprising devices, device components, steps or routines in a method embodied in software, or combinations of hardware and software.

In some embodiments the computer-readable storage devices, mediums, and memories can include a cable or wireless signal containing a bit stream and the like. However, when mentioned, non-transitory computer-readable storage media expressly exclude media such as energy, carrier signals, electromagnetic waves, and signals per se.

Methods according to the above-described examples can be implemented using computer-executable instructions that are stored or otherwise available from computer readable media. Such instructions can comprise, for example, instructions and data which cause or otherwise configure a general purpose computer, special purpose computer, or special purpose processing device to perform a certain function or group of functions. Portions of computer resources used can be accessible over a network. The computer executable instructions may be, for example, binaries, intermediate format instructions such as assembly language, firmware, or source code. Examples of computer-readable media that may be used to store instructions, information used, and/or information created during methods according to described examples include magnetic or optical disks, flash memory, USB devices provided with non-volatile memory, networked storage devices, and so on.

Devices implementing methods according to these disclosures can comprise hardware, firmware and/or software, and can take any of a variety of form factors. Typical examples of such form factors include laptops, smart phones, small form factor personal computers, personal digital assistants, rackmount devices, standalone devices, and so on. Functionality described herein also can be embodied in peripherals or add-in cards. Such functionality can also be implemented on a circuit board among different chips or different processes executing in a single device, by way of further example.

The instructions, media for conveying such instructions, computing resources for executing them, and other structures for supporting such computing resources are means for providing the functions described in these disclosures.

Although a variety of examples and other information was used to explain aspects within the scope of the appended claims, no limitation of the claims should be implied based on particular features or arrangements in such examples, as one of ordinary skill would be able to use these examples to derive a wide variety of implementations. Further and although some subject matter may have been described in language specific to examples of structural features and/or method steps, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to these described features or acts. For example, such functionality can be distributed differently or performed in components other than those identified herein. Rather, the described features and steps are disclosed as examples of components of systems and methods within the scope of the appended claims.

Claim language reciting “at least one of” refers to at least one of a set and indicates that one member of the set or multiple members of the set satisfy the claim. For example, claim language reciting “at least one of A and B” means A, B, or A and B. 

What is claimed is:
 1. A method comprising: determining a confidence score for at least one value of parameters of a network environment, wherein the at least one value of the parameters of the network environment defines network events occurring in the network environment; monitoring the confidence score over time to detect an anomaly in the network environment; in response to detecting the anomaly, identifying a relevant network state of the network environment by gathering characteristics of a relevant portion of the network environment within a specific time frame when the anomaly occurred in the network environment; and presenting the relevant network state and the anomaly in the network environment.
 2. The method of claim 1, wherein the confidence score corresponds to at least one of the network events being a failing network event.
 3. The method of claim 1, wherein the anomaly is detected by comparing a current value of the confidence score with one or more past values of the confidence score.
 4. The method of claim 1, further comprising: determining the relevant portion of the network environment based on the at least one value of parameters of the network environment; and identifying the relevant network state of the network environment in response to detecting the anomaly in the network environment, the relevant network state of the network environment identified based on the relevant portion of the network environment and the anomaly in the network environment.
 5. The method of claim 4, further comprising: gathering data from the relevant portion of the network environment; and identifying the relevant network state from the data.
 6. The method of claim 1, further comprising: determining the specific time frame the anomaly occurred in the network environment; and identifying the relevant network state of the network environment in response to detecting the anomaly using the specific time frame the anomaly occurred in the network environment.
 7. The method of claim 1, wherein the relevant portion of the network environment includes a portion of the network environment where the anomaly occurred.
 8. The method of claim 1, further comprising: correlating the relevant network state with an occurrence of the anomaly; and storing data indicating a correlation of the relevant network state with the occurrence of the anomaly.
 9. The method of claim 1, wherein the anomaly is detected by monitoring a Gaussian distribution of the confidence score over time.
 10. A system comprising: one or more processors; and a computer-readable medium comprising instructions stored therein, which when executed by the one or more processors, cause the one or more processors to: determine a confidence score for at least one value of parameters of a network environment, wherein the at least one value of the parameters of the network environment defines network events occurring in the network environment; monitor the confidence score over time to detect an anomaly in the network environment; in response to detecting the anomaly, identify a relevant network state of the network environment by gathering characteristics of a relevant portion of the network environment within a specific time frame when the anomaly occurred in the network environment; and present the relevant network state and the anomaly in the network environment.
 11. The system of claim 10, wherein the confidence score corresponds to at least one of the network events being a failing network event.
 12. The system of claim 10, wherein the anomaly is detected by comparing a current value of the confidence score with one or more past values of the confidence score.
 13. The system of claim 10, wherein the instructions, which when executed by the one or more processors, further cause the one or more processors to: determine the relevant portion of the network environment based on the at least one value of parameters of the network environment; and identify the relevant network state of the network environment in response to detecting the anomaly in the network environment, the relevant network state of the network environment identified based on the relevant portion of the network environment and the anomaly in the network environment.
 14. The system of claim 13, wherein the instructions, which when executed by the one or more processors, further cause the one or more processors to: gather data from the relevant portion of the network environment; and identify the relevant network state from the data.
 15. The system of claim 10, wherein the instructions, which when executed by the one or more processors, further cause the one or more processors to: determine the specific time frame the anomaly occurred in the network environment; and identify the relevant network state of the network environment in response to detecting the anomaly using the specific time frame the anomaly occurred in the network environment.
 16. The system of claim 10, wherein the relevant portion of the network environment includes a portion of the network environment where the anomaly occurred.
 17. The system of claim 10, wherein the instructions, which when executed by the one or more processors, further cause the one or more processors to: correlate the relevant network state with an occurrence of the anomaly; and store data indicating a correlation of the relevant network state with the occurrence of the anomaly.
 18. The system of claim 10, wherein the anomaly is detected by monitoring a Gaussian distribution of the confidence score over time.
 19. A non-transitory computer-readable storage medium comprising instructions stored therein, which when executed by one or more processors, cause the one or more processors to: determine a confidence score for at least one value of parameters of a network environment, wherein the at least one value of the parameters of the network environment defines network events occurring in the network environment; monitor the confidence score over time to detect an anomaly in the network environment; in response to detecting the anomaly, identify a relevant network state of the network environment by gathering characteristics of a relevant portion of the network environment within a specific time frame when the anomaly occurred in the network environment; and present the relevant network state and the anomaly in the network environment.
 20. The non-transitory computer-readable storage medium of claim 19, wherein the instructions, which when executed by the one or more processors, cause the one or more processors to: correlate the relevant network state with an occurrence of the anomaly; and store data indicating a correlation of the relevant network state with the occurrence of the anomaly. 